Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis

•Flash-flood susceptibility modeling was done using highly accurate models.•A number of 111 flash-flood locations were used for modelling.•A number of 8 flash-flood predictors were used to estimate the susceptibility.•DLNN-AHP was the most performant machine learning model.•Flash-flood susceptibility maps achieved a very good prediction accuracy. The present study was done in order to simulate the flash-flood susceptibility across the Suha river basin in Romania using a number of 3 hybrid models and fuzzy-AHP multicriteria decision-making analysis. It should be noted that flash-flood events are triggered by heavy rainfall in small river catchments. To achieve the proposed results, a total of 8 flash-flood predictors (slope angle, plan curvature, hydrological soil groups, land use, convergence index, profile curvature, topographic position index, aspect) along with a sample of 111 torrential phenomena points were used as input datasets in the next four algorithms: Fuzzy-Analytical Hierarchy Process (FAHP), Deep Learning Neural Network -Analytical Hierarchy Process (DLNN-AHP), Multilayer Perceptron - Analytical Hierarchy Process (MLP-AHP) and Naïve Bayes - Analytical Hierarchy Process (NB-AHP). The Analytical Hierarchy Process was used to calculate the coefficients for each class/category of flash-flood predictors. The torrential points sample was split into training (70%) and validating samples (30%). The modelling was done in Excel, SPSS and R software (H2O package), while the result mapping was performed in ArcGIS 10.5 software. The analysis revealed that the high and very high susceptibility degrees are spread over a maximum of 35.01% of the study area. The best performances, demonstrated by an AUC-ROC of 0.984, are associated with the Deep Learning Neural Network – Analytical Hierarchy Process model, followed by Naïve Bayes – Analytical Hierarchy Process model (AUC = 0.976), Multilayer Perceptron - Analytical Hierarchy Process model (AUC = 0.882) and Fuzzy-Analytical Hierarchy Process (AUC = 0.807). These results indicates that Deep Learning Neural Network is a promising machine learning model which can provide outcomes with very high precision. Also, according to the present research results the deep learning neural network, having many hidden layers, is able outperform the multilayer perceptron that contains a single hidden layer. The main novelty of the present research is the application of the three ensemble models (DLNN-AHP, MLP-AHP and NB-AHP) an